Description

Key Responsibilities: Data Collection & Cleaning: Assist in gathering, cleaning, and preprocessing large datasets from various sources (e.g., databases, APIs, CSV files) to ensure data quality and consistency. Exploratory Data Analysis (EDA): Conduct exploratory analysis to identify trends, patterns, and relationships in the data. Help in visualizing data insights through graphs and charts using tools like Python (Matplotlib, Seaborn) or R. Statistical Analysis: Apply statistical methods to identify correlations, perform hypothesis testing, and provide actionable insights based on data. Model Building & Evaluation: Assist in building machine learning models (classification, regression, clustering, etc.) using Python (scikit-learn, TensorFlow, etc.) or R. Help in tuning and optimizing models for better performance. Reporting & Visualization: Help create clear and concise reports and dashboards to communicate data-driven insights to business stakeholders using tools like Power BI, Tableau, or Jupyter Notebooks. Collaboration: Work closely with senior data scientists, engineers, and business teams to understand requirements and deliver solutions. Participate in code reviews and discussions to improve processes. Learning & Development: Stay updated with the latest trends and techniques in data science, machine learning, and AI. Participate in training sessions and improve your technical skills. Skills and Qualifications: Educational Background: Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related field. Programming Skills: Familiarity with programming languages like Python or R for data manipulation, analysis, and modeling. Experience with libraries like Pandas, NumPy, and scikit-learn is a plus. Mathematics & Statistics: Strong foundation in statistics, probability, and linear algebra. Understanding of key statistical techniques like regression, hypothesis testing, etc. Machine Learning: Basic knowledge of machine learning algorithms (e.g., decision trees, SVMs, k-NN, clustering) and their practical applications. Data Visualization: Experience or interest in using tools like Matplotlib, Seaborn, Power BI, Tableau, or others to create visualizations. Database Knowledge: Basic knowledge of SQL for querying relational databases. Problem-Solving Mindset: Strong analytical thinking and problem-solving skills. Ability to approach problems systematically and creatively. Communication: Good written and verbal communication skills to convey complex data-driven insights to non-technical stakeholders.

Role and Responsibilities

• Key Responsibilities: Data Collection & Cleaning: Assist in gathering, cleaning, and preprocessing large datasets from various sources (e.g., databases, APIs, CSV files) to ensure data quality and consistency. Exploratory Data Analysis (EDA): Conduct exploratory analysis to identify trends, patterns, and relationships in the data. Help in visualizing data insights through graphs and charts using tools like Python (Matplotlib, Seaborn) or R. Statistical Analysis: Apply statistical methods to identify correlations, perform hypothesis testing, and provide actionable insights based on data. Model Building & Evaluation: Assist in building machine learning models (classification, regression, clustering, etc.) using Python (scikit-learn, TensorFlow, etc.) or R. Help in tuning and optimizing models for better performance. Reporting & Visualization: Help create clear and concise reports and dashboards to communicate data-driven insights to business stakeholders using tools like Power BI, Tableau, or Jupyter Notebooks. Collaboration: Work closely with senior data scientists, engineers, and business teams to understand requirements and deliver solutions. Participate in code reviews and discussions to improve processes. Learning & Development: Stay updated with the latest trends and techniques in data science, machine learning, and AI. Participate in training sessions and improve your technical skills. Skills and Qualifications: Educational Background: Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related field. Programming Skills: Familiarity with programming languages like Python or R for data manipulation, analysis, and modeling. Experience with libraries like Pandas, NumPy, and scikit-learn is a plus. Mathematics & Statistics: Strong foundation in statistics, probability, and linear algebra. Understanding of key statistical techniques like regression, hypothesis testing, etc. Machine Learning: Basic knowledge of machine learning algorithms (e.g., decision trees, SVMs, k-NN, clustering) and their practical applications. Data Visualization: Experience or interest in using tools like Matplotlib, Seaborn, Power BI, Tableau, or others to create visualizations. Database Knowledge: Basic knowledge of SQL for querying relational databases. Problem-Solving Mindset: Strong analytical thinking and problem-solving skills. Ability to approach problems systematically and creatively. Communication: Good written and verbal communication skills to convey complex data-driven insights to non-technical stakeholders.